Testing a Cloud-Based Model for Active Surveillance of Medical Devices with Analyses of Coronary Stent Safety Using the Data Extraction and Longitudinal Trend Analysis (DELTA) System.

Abstract

Objective: To demonstrate the use of the Data Extraction and Longitudinal Trend Analysis (DELTA) system in the National Evaluation System for health Technology's (NEST) medical device surveillance cloud environment by analyzing coronary stent safety using real world clinical data and comparing results to clinical trial findings.

Design and setting: Electronic health record (EHR) data from two health systems, the Social Security Death Master File, and device databases were ingested into the NEST cloud, and safety analyses of two stents were performed using DELTA.

Participants and interventions: This is an observational study of patients receiving zotarolimus drug-eluting coronary stents (ZES) or everolimus eluting coronary stents (EES) between July 1, 2015 and December 31, 2017.

Results: After exclusions, 3334 patients receiving EES and 1002 receiving ZES were available for study. Analysis using inverse probability weighting showed no significant difference in one-year mortality or major adverse cardiac events (MACE) for EES compared to ZES [Mortality Odds Ratio 0.94 (95% CI 0.81-1.175); p = 0.780] [MACE Odds Ratio 1.04 (95% CI 0.92-1.16; p = 0.551]). Analysis using propensity matching showed no significant difference in EES one-year mortality (547 of 992 alive and available after censoring) compared to ZES (546 of 992) [Log-Rank statistic 0.3348 (p = 0.563)].

Conclusion: Automated cloud-based medical device safety surveillance using EHR data is feasible and was efficiently performed using DELTA. No statistically significant differences in 1-year safety outcomes between ZES and EES were identified using two statistical approaches, consistent with randomized trial findings.